Depth Map Intra Coding Prediction Using Partitioned Sample Selection
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Solution Overview
Problem
Current video coding techniques for depth maps in 3D video coding face challenges in computational complexity and accuracy due to the need for averaging all neighboring samples to determine predicted depth values, especially for larger block sizes, which increases operational complexity and can introduce prediction errors near partition boundaries.
Innovation Solution
The proposed method determines a predicted depth value by analyzing a partitioning pattern to select representative neighboring samples rather than averaging all samples, reducing the number of operations and improving accuracy by avoiding samples near partition boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all neighboring samples are averaged to determine predicted depth values, then prediction accuracy may be improved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts only the most relevant neighboring samples (first and last samples of each partition) to form the prediction, discarding the intermediate samples that would otherwise be processed. This selective extraction reduces the number of operations from averaging all samples to averaging just two samples per partition, significantly lowering computational complexity while maintaining prediction accuracy.
2Loss of information
If all neighboring samples are used for prediction, then more information is available, but prediction errors near partition boundaries increase
Solution Approach 1:
The patent segments the block of depth values into multiple partitions and applies different prediction strategies to each partition. By using only the first and last samples of each partition for prediction, the method avoids using samples near partition boundaries that would introduce prediction errors, while still maintaining adequate information availability through the segmented approach.
3Productivity
If the number of operations is reduced by selecting fewer samples, then computational complexity decreases, but prediction accuracy may deteriorate
Solution Approach 1:
The patent applies local quality by treating different regions (partitions) of the block differently. For each partition, it selectively uses only the boundary samples (first and last) that are most representative of that local region, rather than uniformly processing all samples. This local approach maintains prediction accuracy for each partition while reducing overall computational complexity.
Data Source
AI summary
In an example, a process for coding video data includes determining a partitioning pattern for a block of depth values comprising assigning one or more samples of the block to a first partition and assigning one or more other samples of the block to a second partition. The process also includes determining a predicted value for at least one of the first partition and the second partition based on the determined partition pattern. The process also includes coding the at least one of the first partition and the second partition based on the predicted value.